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Copy number variation (CNV) is pervasive in the human genome and has been shown to contribute significantly to phenotypic diversity and disease aetiology. High-throughput sequencing (HTS) technologies have allowed for the systematic investigation of CNV at an unprecedented resolution. HTS studies offer multiple distinct features that can provide evidence for the presence of CNV. We have developed an integrative statistical framework that jointly analyses multiple sequencing features at the population level to achieve sensitive and precise discovery of CNV. First, we applied our framework to low-coverage whole-genome sequencing experiments and used data from the 1000 Genomes Project to demonstrate a substantial improvement in CNV detection accuracy over existing methods. Next, we extended our approach to targeted HTS experiments, which offer improved cost-efficiency by focusing on a predetermined subset of the genome. Targeted HTS involves an enrichment step that introduces non-uniformity in sequencing coverage across target regions and thus hinders CNV identification. To that end, we designed a customized normalization procedure that counteracts the effects of enrichment bias and enhances the underlying CNV signal. Our extended framework was benchmarked on contiguous capture datasets, where it was shown to outperform competing strategies by a wide margin. Capture sequencing can also generate large amounts of data in untargeted genomic regions. Although these off-target results can be a valuable source of CNV evidence, they are subject to complex enrichment patterns that confound their interpretation. Therefore, we developed the first normalization strategy that can adapt to the highly heterogeneous nature of off-target capture and thus facilitate CNV investigation in untargeted regions. All in all, we present a generalized CNV detection toolset that has been shown to achieve robust performance across datasets and sequencing platforms and can therefore provide valuable insight into the prevalence and impact of CNV.

The scientific interest in copy number variation (CNV) is rapidly increasing, mainly due to the evidence of phenotypic effects and its contribution to disease susceptibility. Single nucleotide polymorphisms (SNPs) which are abundant in the human genome have been widely investigated in genome-wide association studies (GWAS). Despite the notable genomic effects both CNVs and SNPs have, the correlation between them has been relatively understudied. In the past decade, next generation sequencing (NGS) has been the leading high-throughput technology for investigating CNVs and offers mapping at a high-quality resolution. We created a map of NGS-defined CNVs tagged by SNPs using the 1000 Genomes Project phase 3 (1000G) sequencing data to examine patterns between the two types of variation in protein-coding genes. To investigate potential relationships between CNV-tagging SNPs and various phenotypes, we used SNPs reported for disease/phenotype associations from the GWAS catalog. Moreover, we applied our method to DIAGRAM consortium and Northern Finland Birth Cohort (NFBC) data. Our analysis replicated existing CNV-tagging SNPs but also revealed novel relationships between them in almost all the datasets we analysed. We have developed a statistical framework under a population perspective for a fast and accurate CNV detection. Using 202 drug-target genes defined in collaboration with GlaxoSmithKline (GSK), we applied our framework to the 1000G data. We calculated summary statistics based on the detected CNV calls including the allele frequency (AF) for each of the 26 populations of the 1000G. In addition, we visualised our results using UCSC genome browser visualisation tracks for all 202 regions and successfully benchmarked our CNV calls by comparing them to a gold standard set of the 1000G CNVs. Overall in this thesis, we present detailed maps of CNVs and CNV-tagging SNPs to enhance existing knowledge of their impact on human genome.